LLM vulnerabilities: What breaks, why it matters, and how to spot them

When you ask a large language model, an AI system that generates human-like text by predicting the next word in a sequence. Also known as LLM, it answers questions, writes emails, or even codes for you—it feels like magic. But that magic has cracks. LLM vulnerabilities, flaws in how these models process inputs, store data, or respond to manipulation aren’t theoretical. They’re causing real harm: fake citations in research papers, leaked customer data from chatbots, and AI agents doing exactly what they’re told—even when it’s dangerous.

One of the most common exploits is prompt injection, a technique where attackers sneak hidden commands into user input to override the model’s rules. Think of it like whispering a secret code to a very polite assistant who’s programmed to obey every request. The model doesn’t know it’s being tricked—it just follows the new instructions. This is why AI tools that generate reports or handle customer service can be hijacked to spill internal secrets or write harmful content. Then there’s AI hallucinations, when models confidently invent facts, sources, or data that never existed. It’s not a bug—it’s a feature of how LLMs work. But when a doctor uses an AI to check drug interactions and gets a fake study citation, that’s not just misleading—it’s life-threatening. And behind the scenes, LLM data privacy, the risk that models retain and leak personal information from training data, means your company’s internal emails or customer addresses might be buried in the model’s memory, waiting to be pulled out by the right query.

These aren’t isolated problems. They’re connected. A model with poor data hygiene leads to hallucinations. A model without input filtering invites prompt injection. And both can be worsened by over-reliance on automation without human checks. The posts below don’t just list these issues—they show you exactly how they happen in real systems, how to test for them, and what controls actually work. You’ll see how companies are catching fake citations before they go public, how teams are locking down LLM inputs to block manipulation, and why memory management isn’t just a tech problem—it’s a legal one under GDPR and PIPL. This isn’t about fear. It’s about building AI that doesn’t break when you need it most.